بهینه سازی شاخص گذاری منابع خوشه ای اینترنت اشیا بر اساس الگوریتم کلونی مورچه بهبود یافته Optimization of cluster resource indexing of Internet of Things based on improved ant colony algorithm

1 Introduction With the development of Internet of Things technology, Internet of Things received considerable attention in resource scheduling and transmission with good real-time and strong object-oriented ability [1–3]. There is massive cluster resources in Internet of Things platform, so it is required to optimize the scheduling and retrieval of massive cluster resources, and improve classification management and information processing capacity of resources [4–6]. The client of cluster resources in Internet of Things is done with multi-host and multi-database distribution to meet the demands of distributed storage and retrieval of cluster resources in Internet of Things. With the expansion of resource scale, the difficulty of indexing cluster resources in Internet of Things is larger. It is of great significance to study a more effective cluster resource indexing method for Internet of Things in improving resource scheduling and data transmission and reception of Internet of Things, so relative researches on resource indexing methods have received a great attention [7–9]. At present, the research on the development of cluster resource indexing of Internet of Things is based on database retrieval and optimal design of routing mechanism of Internet of Things. Data source, business logic, user interface and communication protocols are bundled together with the distribution model of link routing constructing Internet of Things in cellular, self-organizing and mixed way to achieve optimal retrieval of cluster resources [10–12]. Common cluster resource index methods include feature labeling method of spatial information points, fuzzy retrieval method, data clustering method and particle swarm optimiza tion index method [13–15]. Some research achievement has been achieved in self-adaptive scheduling and classification indexing for cluster resource with web crawler and data clustering. In this paper, a method of indexing cluster resources of Internet of Things based on multi-layer fuzzy subtraction clustering algorithm is proposed in literature [16]. Combined with the improved particle swarm optimization, this proposed method can prevent the interference of neighboring data points and improves the security of Internet of Things information management platform in cluster resource indexing of Internet of Things, but the method is not high in the precision of large-scale resource indexing; In literature [17], a method of indexing cluster resources of Internet of Things based on association semantic fusion clustering is proposed, and association semantic fusion clustering is done with segmented fusion fuzzy clustering method to construct index target values and calculate the global optimal solution. And then the cluster resources of Internet of Things are optimized and indexed. The method has strong anti-interference ability in resource indexing, and its convergence is well during indexing, but this method will bring a great computational cost and its real-time performance of cluster resource indexing is poor.